Modern data analytics applications, e.g. Internet-scale indexing, system trace analysis, recommender engines to name a few, operate on massive amounts of data and call for a parallel approach to data processing. In this work, we focus on the popular MapReduce framework to carry out such tasks and identify bulk data insert operations as a critical preliminary step to achieve reduced processing times, especially when new data is generated and processed at regular time intervals. We present a parallel approach to bulk data insertion in a system that use horizontally range partitioned data and evaluate several variants to insertion operations, including legacy approaches. Our method exploits the parallel processing framework itself to insert data into the system, which is stored in a semi-structured format. Our results indicate that a parallel approach to bulk insertion can substantially reduce the recurrent costs of insertion of new data into the system.
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